AI 中文总结
提出特征空间汇流方法，将拓扑感知状态空间算子嵌入预报动态，在特征空间中显式编码河流网络结构，实现端到端学习的分布式水文建模，显著提升无测站河流流量预报技能并达到最先进水平。
AI 中文摘要
准确预测河流流量和洪水极具挑战性。河流动态受到不同时空尺度下的蓄水、气象强迫和流量传播的影响。因此，预报需要一个考虑通过河流网络在网格单元和流域之间进行上游到下游流动的框架。这种建模在水文学中被称为分布式建模和汇流。现有的深度学习方法要么忽略这种拓扑结构，要么在集总流域上操作，要么通过单独的图或物理汇流模型来路由预测的物理量。我们转而引入特征空间汇流：一种直接嵌入预报动态中的拓扑感知状态空间算子。在每个预报步骤中，该算子从上游网格单元收集潜在状态，并根据已知的河流网络因果地更新下游状态。这保留了河流系统的物理连通性，同时允许被传播的状态本身以端到端方式学习，并使模型能够同时考虑局部动态和邻近上游贡献来预测河流流量。为了解决不确定性并提供概率预报，我们将公平连续排名概率分数（fCRPS）最小化作为训练目标。我们在欧洲洪水预警系统（EFAS）和观测数据上的河流流量预报实验表明，在特征空间中显式编码河流网络的物理结构显著提高了预报技能，特别是在无测站设置中。我们的方法在再分析和观测数据上都达到了最先进的结果，并能够以1角分和6小时间隔预报最长10天提前期的河流流量图。
英文摘要
Accurate forecasting of river discharge and floods is very challenging. River dynamics are affected by storage, meteorological forcing, and flow propagation at different spatial and temporal scales. Forecasting thus requires a framework that considers the upstream-to-downstream flow through river networks across grid cells and catchments. This modeling is known in hydrology as distributed modeling and routing. Existing deep learning approaches either ignore this topology, operate on lumped catchments, or route predicted physical quantities through a separate graph or physical routing model. We instead introduce feature-space routing: a topology-aware state-space operator embedded directly in the forecasting dynamics. At every forecast step, the operator gathers latent states from upstream grid cells and causally updates the downstream state according to the known river network. This preserves the physical connectivity of the river system while allowing the propagated state itself to be learned end-to-end and allows the model to predict river discharge considering both local dynamics and neighboring upstream contributions. To address uncertainty and provide probabilistic forecasts, we minimize the fair continuous ranked probability score (fCRPS) as a training objective. Our experiments on the European Flood Awareness System (EFAS) and observational data for river discharge forecasting demonstrate that encoding the physical structure of river networks explicitly in the feature space substantially improves the forecasting skill, particularly in an ungauged setting. Our approach achieves state-of-the-art results on both reanalysis and observational data and is able to forecast maps of river discharge at 1 arcminute and 6-hourly resolution up to 10 days lead time.
